Open Biological And Biomedical Ontologies Research Articles

Coordinating virus research: The Virus Infectious Disease Ontology.

The COVID-19 pandemic prompted immense work on the investigation of the SARS-CoV-2 virus. Rapid, accurate, and consistent interpretation of generated data is thereby of fundamental concern. Ontologies-structured, controlled, vocabularies-are designed to support consistency of interpretation, and thereby to prevent the development of data silos. This paper describes how ontologies are serving this purpose in the COVID-19 research domain, by following principles of the Open Biological and Biomedical Ontology (OBO) Foundry and by reusing existing ontologies such as the Infectious Disease Ontology (IDO) Core, which provides terminological content common to investigations of all infectious diseases. We report here on the development of an IDO extension, the Virus Infectious Disease Ontology (VIDO), a reference ontology covering viral infectious diseases. We motivate term and definition choices, showcase reuse of terms from existing OBO ontologies, illustrate how ontological decisions were motivated by relevant life science research, and connect VIDO to the Coronavirus Infectious Disease Ontology (CIDO). We next use terms from these ontologies to annotate selections from life science research on SARS-CoV-2, highlighting how ontologies employing a common upper-level vocabulary may be seamlessly interwoven. Finally, we outline future work, including bacteria and fungus infectious disease reference ontologies currently under development, then cite uses of VIDO and CIDO in host-pathogen data analytics, electronic health record annotation, and ontology conflict-resolution projects.

KG-Hub-building and exchanging biological knowledge graphs.

Knowledge graphs (KGs) are a powerful approach for integrating heterogeneous data and making inferences in biology and many other domains, but a coherent solution for constructing, exchanging, and facilitating the downstream use of KGs is lacking. Here we present KG-Hub, a platform that enables standardized construction, exchange, and reuse of KGs. Features include a simple, modular extract-transform-load pattern for producing graphs compliant with Biolink Model (a high-level data model for standardizing biological data), easy integration of any OBO (Open Biological and Biomedical Ontologies) ontology, cached downloads of upstream data sources, versioned and automatically updated builds with stable URLs, web-browsable storage of KG artifacts on cloud infrastructure, and easy reuse of transformed subgraphs across projects. Current KG-Hub projects span use cases including COVID-19 research, drug repurposing, microbial-environmental interactions, and rare disease research. KG-Hub is equipped with tooling to easily analyze and manipulate KGs. KG-Hub is also tightly integrated with graph machine learning (ML) tools which allow automated graph ML, including node embeddings and training of models for link prediction and node classification. https://kghub.org.

Ontologizing health systems data at scale: making translational discovery a reality

Common data models solve many challenges of standardizing electronic health record (EHR) data but are unable to semantically integrate all of the resources needed for deep phenotyping. Open Biological and Biomedical Ontology (OBO) Foundry ontologies provide computable representations of biological knowledge and enable the integration of heterogeneous data. However, mapping EHR data to OBO ontologies requires significant manual curation and domain expertise. We introduce OMOP2OBO, an algorithm for mapping Observational Medical Outcomes Partnership (OMOP) vocabularies to OBO ontologies. Using OMOP2OBO, we produced mappings for 92,367 conditions, 8611 drug ingredients, and 10,673 measurement results, which covered 68–99% of concepts used in clinical practice when examined across 24 hospitals. When used to phenotype rare disease patients, the mappings helped systematically identify undiagnosed patients who might benefit from genetic testing. By aligning OMOP vocabularies to OBO ontologies our algorithm presents new opportunities to advance EHR-based deep phenotyping.

The Environmental Conditions, Treatments, and Exposures Ontology (ECTO): connecting toxicology and exposure to human health and beyond

BackgroundEvaluating the impact of environmental exposures on organism health is a key goal of modern biomedicine and is critically important in an age of greater pollution and chemicals in our environment. Environmental health utilizes many different research methods and generates a variety of data types. However, to date, no comprehensive database represents the full spectrum of environmental health data. Due to a lack of interoperability between databases, tools for integrating these resources are needed. In this manuscript we present the Environmental Conditions, Treatments, and Exposures Ontology (ECTO), a species-agnostic ontology focused on exposure events that occur as a result of natural and experimental processes, such as diet, work, or research activities. ECTO is intended for use in harmonizing environmental health data resources to support cross-study integration and inference for mechanism discovery.Methods and findingsECTO is an ontology designed for describing organismal exposures such as toxicological research, environmental variables, dietary features, and patient-reported data from surveys. ECTO utilizes the base model established within the Exposure Ontology (ExO). ECTO is developed using a combination of manual curation and Dead Simple OWL Design Patterns (DOSDP), and contains over 2700 environmental exposure terms, and incorporates chemical and environmental ontologies. ECTO is an Open Biological and Biomedical Ontology (OBO) Foundry ontology that is designed for interoperability, reuse, and axiomatization with other ontologies. ECTO terms have been utilized in axioms within the Mondo Disease Ontology to represent diseases caused or influenced by environmental factors, as well as for survey encoding for the Personalized Environment and Genes Study (PEGS).ConclusionsWe constructed ECTO to meet Open Biological and Biomedical Ontology (OBO) Foundry principles to increase translation opportunities between environmental health and other areas of biology. ECTO has a growing community of contributors consisting of toxicologists, public health epidemiologists, and health care providers to provide the necessary expertise for areas that have been identified previously as gaps.

The Epilepsy Ontology: a community-based ontology tailored for semantic interoperability and text mining.

Epilepsy is a multifaceted complex disorder that requires a precise understanding of the classification, diagnosis, treatment and disease mechanism governing it. Although scattered resources are available on epilepsy, comprehensive and structured knowledge is missing. In contemplation to promote multidisciplinary knowledge exchange and facilitate advancement in clinical management, especially in pre-clinical research, a disease-specific ontology is necessary. The presented ontology is designed to enable better interconnection between scientific community members in the epilepsy domain. The Epilepsy Ontology (EPIO) is an assembly of structured knowledge on various aspects of epilepsy, developed according to Basic Formal Ontology (BFO) and Open Biological and Biomedical Ontology (OBO) Foundry principles. Concepts and definitions are collected from the latest International League against Epilepsy (ILAE) classification, domain-specific ontologies and scientific literature. This ontology consists of 1879 classes and 28 151 axioms (2171 declaration axioms, 2219 logical axioms) from several aspects of epilepsy. This ontology is intended to be used for data management and text mining purposes. The current release of the ontology is publicly available under a Creative Commons 4.0 License and shared via http://purl.obolibrary.org/obo/epso.owl and is a community-based effort assembling various facets of the complex disease. The ontology is also deposited in BioPortal at https://bioportal.bioontology.org/ontologies/EPIO. Supplementary data are available at Bioinformatics Advances online.

Developing a pneumonia diagnosis ontology from multiple knowledge sources.

Background: Pneumonia is difficult to differentiate from other pulmonary diseases because it shares many symptoms with these diseases. Diagnosing pneumonia in clinical practice would benefit from having access to a codified representation of clinical knowledge. An ontology represents a well-established paradigm for such codification. Objectives: The goal of this research is to create Pneumonia Diagnosis Ontology (PNADO) that brings together the medical knowledge dispersed among multiple medical knowledge sources. Material and Methods: We used several clinical practice guidelines (CPGs) describing the pneumonia diagnostic process as a starting point in developing PNADO. Preliminary version of PNADO was subsequently expanded to cover a broader range of the concepts by reusing ontologies from Open Biological and Biomedical Ontology (OBO) Foundry and BioPortal. PNADO was evaluated by examining relevant concepts from the pneumonia-specific systematic reviews, using patient data from the MIMIC-III clinical dataset, and by clinical domain experts. Results: PNADO is a comprehensive ontology and has a rich set of classes and properties that cover different types of pneumonia, pathogens, symptoms, clinical signs, laboratory tests and imaging, clinical findings, complications, and diagnoses. Conclusion: PNADO unifies pneumonia diagnostic concepts from multiple knowledge sources. It is available in the BioPortal repository.

CEBS update: curated toxicology database with enhanced tools for data integration.

The Chemical Effects in Biological Systems database (CEBS) contains extensive toxicology study results and metadata from the Division of the National Toxicology Program (NTP) and other studies of environmental health interest. This resource grants public access to search and collate data from over 10 250 studies for 12 750 test articles (chemicals, environmental agents). CEBS has made considerable strides over the last 5 years to integrate growing internal data repositories into data warehouses and data marts to better serve the public with high quality curated datasets. This effort includes harmonizing legacy terms and metadata to current standards, mapping test articles to external identifiers, and aligning terms to OBO (Open Biological and Biomedical Ontology) Foundry ontologies. The data are made available through the CEBS Homepage (https://cebs.niehs.nih.gov/cebs/), guided search applications, flat files on FTP (file transfer protocol), and APIs (application programming interface) for user access and to provide a bridge for computational tools. The user interface is intuitive with a single search bar to query keywords related to study metadata, publications, and data availability. Results are consolidated to single pages for each test article with NTP conclusions, publications, individual studies, data collections, and links to related test articles and projects available together.

OBO Foundry in 2021: operationalizing open data principles to evaluate ontologies.

Biological ontologies are used to organize, curate and interpret the vast quantities of data arising from biological experiments. While this works well when using a single ontology, integrating multiple ontologies can be problematic, as they are developed independently, which can lead to incompatibilities. The Open Biological and Biomedical Ontologies (OBO) Foundry was created to address this by facilitating the development, harmonization, application and sharing of ontologies, guided by a set of overarching principles. One challenge in reaching these goals was that the OBO principles were not originally encoded in a precise fashion, and interpretation was subjective. Here, we show how we have addressed this by formally encoding the OBO principles as operational rules and implementing a suite of automated validation checks and a dashboard for objectively evaluating each ontology’s compliance with each principle. This entailed a substantial effort to curate metadata across all ontologies and to coordinate with individual stakeholders. We have applied these checks across the full OBO suite of ontologies, revealing areas where individual ontologies require changes to conform to our principles. Our work demonstrates how a sizable, federated community can be organized and evaluated on objective criteria that help improve overall quality and interoperability, which is vital for the sustenance of the OBO project and towards the overall goals of making data Findable, Accessible, Interoperable, and Reusable (FAIR).Database URL http://obofoundry.org/

Semantic Search in Legacy Biodiversity Literature: Integrating data from different data infrastructures

Nowadays, obtaining information by entering queries into a web search engine is routine behaviour. With its search portal, the Specialised Information Service Biodiversity Research (BIOfid) adapts the exploration of legacy biodiversity literature and data extraction to current standards (Driller et al. 2020). In this presentation, we introduce the BIOfid search portal and its functionalities in a How-To short guide. To this end, we adapted a knowledge graph representation of our thematic focus of Central European, primarily German language, biodiversity literature of the 19th and 20th centuries. Now, users can search our text-mined corpus containing to date more than 8.700 full-text articles from 68 journals, and particularly focussing on birds, lepidopterans and vascular plants. The texts are automatically preprocessed by the Natural Language Processing provider TextImager (Hemati et al. 2016) and will be linked to various databases such as Wikidata, Wikipedia, the Global Biodiversity Information Facility (GBIF), Encyclopedia of Life (EoL), Geonames, the Integrated Authority File (GND) and WordNet. For data retrieval, users can filter search results and download the article metadata as well as text annotations and database links in JavaScript Object Notation (JSON) format. For example, literature that mentions taxa from certain decades or co-occurrences of species can be searched. Our search engine recognises scientific and vernacular taxon names based on the GBIF Backbone Taxonomy and offers search suggestions to support the user. The semantic network of the BIOfid search portal is also enriched with data from the EoL trait bank, so that trait data can be included in the search queries. Thus, scientists can enhance their own data sets with the search results and feed them into the relevant biodiversity data repositories to sustainably expand the corresponding knowledge graphs with reliable data. Since BIOfid applies standard ontology terms, all data mobilized from literature can be combined with data on natural history collection objects or data from current research projects in order to generate more comprehensive knowledge. Furthermore, taxonomy, ecology and trait ontologies that have been built or extended within this project will be made available through appropriate platforms such as The Open Biological and Biomedical Ontology (OBO) Foundry and the Terminology Service of The German Federation for Biological Data (GFBio).

IrGO: Iranian traditional medicine General Ontology and knowledge base

BackgroundIranian traditional medicine, also known as Persian Medicine, is a holistic school of medicine with a long prolific history. It describes numerous concepts and the relationships between them. However, no unified language system has been proposed for the concepts of this medicine up to the present time. Considering the extensive terminology in the numerous textbooks written by the scholars over centuries, comprehending the totality of concepts is obviously a very challenging task. To resolve this issue, overcome the obstacles, and code the concepts in a reusable manner, constructing an ontology of the concepts of Iranian traditional medicine seems a necessity.Construction and contentMakhzan al-Advieh, an encyclopedia of materia medica compiled by Mohammad Hossein Aghili Khorasani, was selected as the resource to create an ontology of the concepts used to describe medicinal substances. The steps followed to accomplish this task included (1) compiling the list of classes via examination of textbooks, and text mining the resource followed by manual review to ensure comprehensiveness of extracted terms; (2) arranging the classes in a taxonomy; (3) determining object and data properties; (4) specifying annotation properties including ID, labels (English and Persian), alternative terms, and definitions (English and Persian); (5) ontology evaluation. The ontology was created using Protégé with adherence to the principles of ontology development provided by the Open Biological and Biomedical Ontology (OBO) foundry.Utility and discussionThe ontology was finalized with inclusion of 3521 classes, 15 properties, and 20,903 axioms in the Iranian traditional medicine General Ontology (IrGO) database, freely available at http://ir-go.net/. An indented list and an interactive graph view using WebVOWL were used to visualize the ontology. All classes were linked to their instances in UNaProd database to create a knowledge base of ITM materia medica.ConclusionWe constructed an ontology-based knowledge base of ITM concepts in the domain of materia medica to help offer a shared and common understanding of this concept, enable reuse of the knowledge, and make the assumptions explicit. This ontology will aid Persian medicine practitioners in clinical decision-making to select drugs. Extending IrGO will bridge the gap between traditional and conventional schools of medicine, helping guide future research in the process of drug discovery.

The Standards behind the Scenes: Explaining data from the Plazi workflow

As part of the CETAF COVID19 task force, Plazi liberated taxonomic treatments, figures, observation records, biotic interactions, taxonomic names, and collection and specimen codes involving bats and viruses from scholarly publications with the intention to create open access, findable, accessible, interoperable and reusable data (FAIR). The data is accessible via TreatmentBank and the Biodiversity Literature Repository (BLR) and it is continually harvested and reused by the Global Biodiversity Information Facility (GBIF) and Global Biotic Interactions (GloBI). This data was processed, enhanced and liberated by the Plazi workflow, which involves a dedicated infrastructure including a desktop application (GoldenGate Imagine) that converts portable document format files (PDF) to a dedicated open compressed file format (Image Markup File (IMF)) that is responsible for the data enhancement. To enhance the data contained in the publications, including the biological interactions, a series of standards and vocabularies are used. To the exception of TaxPub, which is a taxonomic specific extension of the U.S. National Center for Biotechnology Information's (NCBI) Journal Article Tag Suite (JATS), all other used vocabulary were previously proposed. This goes along with Plazi’s mission to reuse standards unless they are not available. The following standards of vocabularies are used: Metadata Object Description Schema (MODS) to model article metadata information on Plazi’s XMLs; Darwin Core for taxonomic ranks and materials citation related data; Open Biological and Biomedical Ontology (OBO); Relations Ontology for biological interactions between organisms. The latter two are also used in the custom metadata in the Biodiversity Literature Repository at Zenodo. In this presentation we will provide an overview of the different types of data followed by the standards or vocabularies applied for every and each one of them and their parts. The goal is to provide the context on how the data liberated by Plazi is described, which is extensively reused by third-party applications such as GBIF or GloBI. The use of the standards allows fully automated, daily data ingests by GBIF.

ROBOT: A Tool for Automating Ontology Workflows

BackgroundOntologies are invaluable in the life sciences, but building and maintaining ontologies often requires a challenging number of distinct tasks such as running automated reasoners and quality control checks, extracting dependencies and application-specific subsets, generating standard reports, and generating release files in multiple formats. Similar to more general software development, automation is the key to executing and managing these tasks effectively and to releasing more robust products in standard forms.For ontologies using the Web Ontology Language (OWL), the OWL API Java library is the foundation for a range of software tools, including the Protégé ontology editor. In the Open Biological and Biomedical Ontologies (OBO) community, we recognized the need to package a wide range of low-level OWL API functionality into a library of common higher-level operations and to make those operations available as a command-line tool.ResultsROBOT (a recursive acronym for “ROBOT is an OBO Tool”) is an open source library and command-line tool for automating ontology development tasks. The library can be called from any programming language that runs on the Java Virtual Machine (JVM). Most usage is through the command-line tool, which runs on macOS, Linux, and Windows. ROBOT provides ontology processing commands for a variety of tasks, including commands for converting formats, running a reasoner, creating import modules, running reports, and various other tasks. These commands can be combined into larger workflows using a separate task execution system such as GNU Make, and workflows can be automatically executed within continuous integration systems.ConclusionsROBOT supports automation of a wide range of ontology development tasks, focusing on OBO conventions. It packages common high-level ontology development functionality into a convenient library, and makes it easy to configure, combine, and execute individual tasks in comprehensive, automated workflows. This helps ontology developers to efficiently create, maintain, and release high-quality ontologies, so that they can spend more time focusing on development tasks. It also helps guarantee that released ontologies are free of certain types of logical errors and conform to standard quality control checks, increasing the overall robustness and efficiency of the ontology development lifecycle.

Semantic Interoperability Solutions for the Essential Variables: Focus on biodiversity

The Earth system is enduring multiple, interacting stressors causing immense and irreversible change to its biosphere. The unprecedented magnitude of human influence on the planet is a cause for much concern, but also an opportunity to mitigate undesirable impact by instituting socio-ecological management strategies and monitoring their effectiveness. To do so, local and global policy developers and decision makers must have access to clear and globally consistent information streams that serve as planetary diagnostics. This need is being addressed by several international, multi-agency networks which have developed sets of "Essential Variables" (EVs) to improve consistency in the observation of key phenomena within their domains of operation. For example, the implementation of the Essential Climate Variables (ECVs) led by the Global Climate Observing System (GCOS) has set an example being followed in the marine and biodiversity domains by, respectively, the Global Ocean Observing System's (GOOS) Essential Ocean Variables (EOVs) and the Group on Earth Observation Biodiversity Observation Network (GEO BON)'s Essential Biodiversity Variables (EBVs). Each of these EV collections has a subset dedicated to biological and ecological phenomena. This is greatly encouraging for biodiversity science at large, but also runs the risk of parallel and conflicting developments occuring in the EV communities addressing thecross-cutting theme of biodiversity. As a consequence, there is an urgent need for these systems, and the communities behind them, to pursue tight conceptual and technical interoperability in aid of a coherent treatment of biodiversity in planetary-scale monitoring. In this contribution, we will describe our current research and implementation of a semantic interoperability layer for the EVs, with a focus on biology and ecology. Applying the best practices and core infrastructure of the Open Biological and Biomedical Ontology (OBO) Foundry and Library, we have leveraged both mature and emerging reference ontologies targeting the domains of ecosystems and environment, population and communities, and high-level ecological phenomena. This interoperable collection of ontologies constitutes a foundation for highly expressive EV knowledge representation and management through robust, FAIR-compliant technologies. Further, we are coordinating these efforts with semantic technology adopted by UN agencies, representing global directives and indicators of the UN Sustainable Development Agenda for 2030. With open editorial models behind each of these ontologies, we aim to offer a scalable and inclusive system to bridge data and information products across the EVs through machine-accessible knowledge representation.

Towards an Ontology of Comparative Biogeography: New insights into the semantics of biodiversity conservation

Climate change, habitat destruction, and myriad other ecological stressors will impact us all and have already contributed to what is being labeled the sixth wave of extinction (Ceballos et al. 2015, Régnier et al. 2015). As a countering force, conservation biology strives to identify those areas of the planet most worthy of protecting due to their unique natural value (Dudley and Stolton 2008). Despite their value, criticisms (Camillo and Peter 2011) have been leveled at 1) the social cost of maintaining protected status (Lele et al. 2010) and 2) instances of continued biodiversity decline despite protection regimes (Craigie et al. 2010, Dudley et al. 2014). At present, the selection and delimitation of protected areas is an intuitive and often subjective process, leading to ambiguities in the semantics behind and across their definitions. Thus, we propose that the application of ontological techniques to the ambiguities in protected area semantics is a timely contribution to conservation informatics. We hold that coherent semantic representation of the biogeographic areas which overlap protected areas (often designated empirically) will provide more efficient and standardized informatics, supporting research and decision-making processes. Our approach draws from comparative biogeography, which seeks to classify biogeographic areas based on their natural properties in a process known as bioregionalisation. In particular, we convert a cladogram of biogeographic areas (similar to cladogram of taxa) into a series of ontological classes, each corresponding to a monophyletic clade of areas. In this model, areas of endemism are treated as formal objects related by hierarchical relationships and constrained by the monophyly condition (Ung 2018). This approach unifies a model-theoretic view of endemism with the semantic web and therefore, offering new possibilities to communicate the biogeographic units conservation. We use semantic web standards (RDF and OWL) expressed through interoperable "Open Biological and Biomedical Ontology (OBO) Foundry" and Library resources to model areas of endemism as evolutionary entities for comparative biogeography. This aligns with current efforts in the OBO Foundry to extend their semantic coverage to the domains of Earth and ecosystem science. Due to our work’s heavy reliance on environmental semantics, we base our work on the Environment Ontology (ENVO), extending it with often confounded biogeographic entities including biogeographic areas, such as areas of endemism and endemic areas, as well as their relationships. Hence, we seek to provide a rigorous and simple framework that improves biogeographic analyses and interoperability between systems.

MCO: towards an ontology and unified vocabulary for a framework-based annotation of microbial growth conditions.

A major component in increasing our understanding of the biology of an organism is the mapping of its genotypic potential into its phenotypic expression profiles. This mapping is executed by the machinery of gene regulation, which is essentially studied by changes in growth conditions. Although many efforts have been made to systematize the annotation of experimental conditions in microbiology, the available annotations are not based on a consistent and controlled vocabulary, making difficult the identification of biologically meaningful comparisons of knowledge derived from different experiments or laboratories. We curated terms related to experimental conditions that affect gene expression in Escherichia coli K-12. Since this is the best-studied microorganism, the collected terms are the seed for the Microbial Conditions Ontology (MCO), a controlled and structured vocabulary that can be expanded to annotate microbial conditions in general. Moreover, we developed an annotation framework to describe experimental conditions, providing the foundation to identify regulatory networks that operate under particular conditions. As far as we know, MCO is the first ontology for growth conditions of any bacterial organism, and it is available at http://regulondb.ccg.unam.mx and https://github.com/microbial-conditions-ontology. Furthermore, we will disseminate MCO throughout the Open Biological and Biomedical Ontology (OBO) Foundry in order to set a standard for the annotation of gene expression data. This will enable comparison of data from diverse data sources. Supplementary data are available at Bioinformatics online.

Enhancing the common model of cognition with social cognitive components – “the rise of the humans”

The intent of this paper is to add inspirational context/understanding for the Common Model of Cognition (CMC) by postulating that the model’s definitions and constituent cognitive concepts are influenced by the social and technology contexts individuals participate in and experience and that evolving this common understanding can be facilitated though collaboration to share data and create consistent definitions of the model and its components using an approach already proven to be successful in the biomedical research community. In order to facilitate collaboration and sharing of research data and enable reaching a consensus on the definitions of the CMC and its components, we propose the collaborative development of a framework of ontologies, such as has been accomplished by the biomedical research community with The Open Biological and Biomedical Ontologies (OBO) Foundry.

Building semantics in the domain of trait data: an OBO Library approach

As the volume and diversity of digitised trait data grows with ever-increasing speed, there is a clear need to capture the knowledge which contextualises it. Many researchers are addressing similar challenges by using ontology-based approaches to represent knowledge and use it to better structure data across resources, however, there is immense variation in how and for what purpose these ontologies are built. While some approaches emphasise quick and lightweight deployment for specific projects, others spend considerable effort in creating heavy duty, finely specified semantics for a wide user base. Effectively ontologising trait data collections is likely to require a hybrid of these strategies and must also consider how to meld emerging efforts with those that have matured into well-adopted, production-oriented systems. This contribution will provide an overview of existng ontologies linked to traits, as well as the best practices used to create and develop them within the Open Biological and Biomedical Ontologies (OBO) Foundry and Library (Smith et al. 2007). Specifically, it will outline a collaborative model for future, open development, based on the domain semantics of the Ontology of Biological Attributes (OBA), the Environment Ontology (ENVO; Buttigieg et al. 2013, Buttigieg et al. 2016b), the Population and Community Ontology (PCO; Walls et al. 2014), and recent work on bridging phenotypes and environments (e.g. Thessen et al. 2015). Finally, perspectives on linking trait semantics, and hence trait data, to societal goals via OBO-aligned efforts to represent the semantics of the United Nations' Sustainable Development Agenda for 2030 (e.g. Buttigieg et al. 2016a) will be offered as a means to bridge scientific data with global socio-ecological goals.

The environment ontology in 2016: bridging domains with increased scope, semantic density, and interoperation.

BackgroundThe Environment Ontology (ENVO; http://www.environmentontology.org/), first described in 2013, is a resource and research target for the semantically controlled description of environmental entities. The ontology's initial aim was the representation of the biomes, environmental features, and environmental materials pertinent to genomic and microbiome-related investigations. However, the need for environmental semantics is common to a multitude of fields, and ENVO's use has steadily grown since its initial description. We have thus expanded, enhanced, and generalised the ontology to support its increasingly diverse applications.MethodsWe have updated our development suite to promote expressivity, consistency, and speed: we now develop ENVO in the Web Ontology Language (OWL) and employ templating methods to accelerate class creation. We have also taken steps to better align ENVO with the Open Biological and Biomedical Ontologies (OBO) Foundry principles and interoperate with existing OBO ontologies. Further, we applied text-mining approaches to extract habitat information from the Encyclopedia of Life and automatically create experimental habitat classes within ENVO.ResultsRelative to its state in 2013, ENVO's content, scope, and implementation have been enhanced and much of its existing content revised for improved semantic representation. ENVO now offers representations of habitats, environmental processes, anthropogenic environments, and entities relevant to environmental health initiatives and the global Sustainable Development Agenda for 2030. Several branches of ENVO have been used to incubate and seed new ontologies in previously unrepresented domains such as food and agronomy. The current release version of the ontology, in OWL format, is available at http://purl.obolibrary.org/obo/envo.owl.ConclusionsENVO has been shaped into an ontology which bridges multiple domains including biomedicine, natural and anthropogenic ecology, ‘omics, and socioeconomic development. Through continued interactions with our users and partners, particularly those performing data archiving and sythesis, we anticipate that ENVO’s growth will accelerate in 2017. As always, we invite further contributions and collaboration to advance the semantic representation of the environment, ranging from geographic features and environmental materials, across habitats and ecosystems, to everyday objects in household settings.

The Ontology of Biological and Clinical Statistics (OBCS) for standardized and reproducible statistical analysis.

BackgroundStatistics play a critical role in biological and clinical research. However, most reports of scientific results in the published literature make it difficult for the reader to reproduce the statistical analyses performed in achieving those results because they provide inadequate documentation of the statistical tests and algorithms applied. The Ontology of Biological and Clinical Statistics (OBCS) is put forward here as a step towards solving this problem.ResultsThe terms in OBCS including ‘data collection’, ‘data transformation in statistics’, ‘data visualization’, ‘statistical data analysis’, and ‘drawing a conclusion based on data’, cover the major types of statistical processes used in basic biological research and clinical outcome studies. OBCS is aligned with the Basic Formal Ontology (BFO) and extends the Ontology of Biomedical Investigations (OBI), an OBO (Open Biological and Biomedical Ontologies) Foundry ontology supported by over 20 research communities. Currently, OBCS comprehends 878 terms, representing 20 BFO classes, 403 OBI classes, 229 OBCS specific classes, and 122 classes imported from ten other OBO ontologies.We discuss two examples illustrating how the ontology is being applied. In the first (biological) use case, we describe how OBCS was applied to represent the high throughput microarray data analysis of immunological transcriptional profiles in human subjects vaccinated with an influenza vaccine. In the second (clinical outcomes) use case, we applied OBCS to represent the processing of electronic health care data to determine the associations between hospital staffing levels and patient mortality. Our case studies were designed to show how OBCS can be used for the consistent representation of statistical analysis pipelines under two different research paradigms. Other ongoing projects using OBCS for statistical data processing are also discussed.The OBCS source code and documentation are available at: https://github.com/obcs/obcs.ConclusionsThe Ontology of Biological and Clinical Statistics (OBCS) is a community-based open source ontology in the domain of biological and clinical statistics. OBCS is a timely ontology that represents statistics-related terms and their relations in a rigorous fashion, facilitates standard data analysis and integration, and supports reproducible biological and clinical research.

The Non-Coding RNA Ontology (NCRO): a comprehensive resource for the unification of non-coding RNA biology.

In recent years, sequencing technologies have enabled the identification of a wide range of non-coding RNAs (ncRNAs). Unfortunately, annotation and integration of ncRNA data has lagged behind their identification. Given the large quantity of information being obtained in this area, there emerges an urgent need to integrate what is being discovered by a broad range of relevant communities. To this end, the Non-Coding RNA Ontology (NCRO) is being developed to provide a systematically structured and precisely defined controlled vocabulary for the domain of ncRNAs, thereby facilitating the discovery, curation, analysis, exchange, and reasoning of data about structures of ncRNAs, their molecular and cellular functions, and their impacts upon phenotypes. The goal of NCRO is to serve as a common resource for annotations of diverse research in a way that will significantly enhance integrative and comparative analysis of the myriad resources currently housed in disparate sources. It is our belief that the NCRO ontology can perform an important role in the comprehensive unification of ncRNA biology and, indeed, fill a critical gap in both the Open Biological and Biomedical Ontologies (OBO) Library and the National Center for Biomedical Ontology (NCBO) BioPortal. Our initial focus is on the ontological representation of small regulatory ncRNAs, which we see as the first step in providing a resource for the annotation of data about all forms of ncRNAs. The NCRO ontology is free and open to all users, accessible at: http://purl.obolibrary.org/obo/ncro.owl.